Lets consider that I want to perform a pan-sharpening using this following images:

Radar image

Classified image

Using Orfeo toolbox I can achieve the following results: (Bayes, LMVM and RCS respectively)

Bayes algorithm

LMVM algorithm

RCS algorithm

Looking at the results made by Orfeo we can choose the RCS output as the best looking pan-sharpened image. In my opinion, it seems to get more details of that part of the land surrounded by water on top of the image, don’t you agree?

Now, the other 2, Bayes and LMVM outputs don’t look quite good in my humble opinion. The Bayes output delivered almost no change and the LMVM output seems a bit blurred.

Considering this, you can try to perform a pan-sharpening operation using HSV fusion, but this is not available neither in QGIS (natively) nor in Orfeo toolbox.

So, having that in mind, my co-workers and I decided to develop a script to perform pan-sharpening using HSV fusion. The result of our work is available as a tool in our QGIS plugin called DSG Tools (https://plugins.qgis.org/plugins/DsgTools/). In another another opportunity I will explain more about DSG Tools.

DSG Tools is a QGIS plugin developed by the Geographic Service at Brazilian Army. It was developed to make it possible to acquire vector data in QGIS in accordance to the Brazilian Geospatial Vector Data Structure, but besides that, DSG Tools provides a considerable toolbox that allow among other things, perform pan-sharpening using HSV fusion.

The script we made can be installed into QGIS’ toolbox and it will be available in a group called DSG as we can see next:

Well, starting the script and setting its parameters (remember to superimpose the images prior the fusion – Orfeo has a tool for this) we can achieve the following result:

In my opinion, the pan-sharpened image generated by our HSV fusion script seems very close to the real terrain. Let’s now compare Orfeo’s RCS output and our HSV fusion output side by side:

RCS algorithm

HSV fusion by DSG Tools

Which one would you choose? I would choose the HSV fusion… But remember, this is just my opinion…

Maybe you’ve already needed to perform a decision tree classification in some point in your GIS life. You might have used ENVI’s decision tree to do the job, right? But, what about QGIS? Can we achieve the same results using QGIS processing? Definitely yes!!!

Today I’ll gonna show how this can be simple.

Imagine you want to classify this image:

Using this one to help filter the land cover:

On ENVI, this can be done with a decision tree like this one (one of my co-workers made it):

Ok, we can see that the decision tree uses several conditions to discover what pixels it should classify in a specific class. This conditions are translated to GRASS using this:

if(x,a,b) a if x not zero, b otherwise

Where “x” is a condition to be followed.

So, to classify water (let’s say pixel value 0) to a class with value 5 and classify everything else to 255 we need to make this expression if(x,5,255), where x is the condition (e.g. image==0).

Quite easy, right?

Moving forward, with more levels (like in the decision tree above), we need to use nested if conditions (i.e if conditions inside another if condition).

So, the next step is to open the processing toolbox in QGIS and search for r.mapcalculator to open the following dialog:

We can choose several images (A, B, and so on…). In my case, I’ll choose Raster Layer A and Raster Layer B (the first two images in this article). Inside the formula I’ll insert my nested if conditions taken from my decision tree, as follows:

With this set, I just need to to click on “Run” and wait for my classified image. By the way, the process is quite fast thanks to GRASS…

As we can see, the results are identical (another point for QGIS and GRASS for the awesome results they deliver together!!!). Below, on the right side we have the QGIS’ result and on the left side we have ENVI’s result (I made a small subset to make it easier to check both results).

We all know that search for errors in geometries can be quite a journey. One of the errors we need to fix is the presence of spikes in our geometries. One way to determine the location of those spikes is to determine the angles and check if them are smaller than a predefined threshold.

Ok them, one way to tackle this problem is to load your data into a PostGIS layer and use the available ST functions.

In this post I’ll show you guys a SQL query to solve this.

Let’s suppose we have geometries with problems like these here:

To solve this problems we will use the following ST PostGIS functions:

We all know that GRASS is a great GIS software. Combined with QGIS it is even more great!

Using GRASS from within QGIS is very useful to deal with daily GIS problems. Everyone that works with geospatial data knows how annoying is to clean up geometries full of errors. The manual process demands lots of time and we can always forget something in the end. Do this kind of job automatically is faster and safer.

Let’s se how to do this using pyqgis. Imagine that we have a database layer like this:

A good way to clean problems like those shown above and at the same time solve snapping problems is to use the following tools in v.clean.advanced provided by GRASS: